Definition of data science consulting

Data science consulting is a service that will help your organisation develop a data science strategy and implement data science solutions.

Executive view

Data science consulting introduces expertise to your organisation that will help you to improve your data science strategy and solutions. You may need a consultant to help you start integrating data science into your business strategy, or to help keep your projects on track.

Data science consulting helps businesses:

• to implement data science.

• to develop a data science strategy.

Business function leader view

Data science consulting helps teams to implement data science and work towards the goals of the organisation’s data science strategy.

You may need this service if:

• you are building a product that could be improved by intelligent features.

• your team has access to large volumes of data but is not using it to make predictions or decisions.

• you are undergoing a data or AI project with a fast turnaround and need expert help.

• you are working on a data science initiative that is not making enough progress, and you need outside expertise.

KPIs you should consider measuring for this are:

• Quicker turnarounds due to more efficient decision-making.

• Increased efficiency when data and insights are shared between departments.

• Increased revenue from acting on data insights.

• Reduced costs due to more efficient data collection, storage and analysis.

Technical view

Even if your team possesses the necessary skills to develop data models for your organisation, you may need the services of a data science consultant to help you deploy the models efficiently and in line with your organisation’s data science strategy.

Data science consulting helps deliver:

• improved performance through analysis of usage data.

• personalised content that can drive increased up-sells and interactions.

• more efficient processes of deploying data science models.

Get this service if you encounter:

• difficulty deploying data models

• difficulty developing a data science strategy

• missed opportunities to gain actionable insights from data

• lack of personalised content in your product

Key criteria to consider are:

• Does your team lack the skills required to develop and deploy data science models?

• Do you have appropriate data storage solutions in place that will keep user data secure?

• Do you have the time and resources to apply your analysis to make improvements to your product?

• What are the skill gaps in your team that a consultant can fill?